Efficient structure-informed featurization and property prediction of ordered, dilute, and random atomic structures

Adam M. Krajewski, Jonathan W. Siegel, Zi Kui Liu

Research output: Contribution to journalArticlepeer-review

Abstract

Structure-informed materials informatics is a rapidly evolving discipline of materials science relying on the featurization of atomic structures or configurations to construct vector, voxel, graph, graphlet, and other representations useful for machine learning prediction of properties, fingerprinting, and generative design. This work discusses how current featurizers typically perform redundant calculations and how their efficiency could be improved by considering (1) fundamentals of crystallographic (orbits) equivalency to optimize ordered structures and (2) representation-dependent equivalency to optimize dilute, doped, and defect structures with broken symmetry. It also discusses and contrasts ways of (3) approximating random solid solutions occupying arbitrary lattices under such representations. Efficiency improvements discussed in this work were implemented within [Formula presented] or python toolset for Structure-Informed Property and Feature Engineering with Neural Networks developed by authors since 2019 and shown to increase performance from 2 to 10 times for typical inputs. Throughout this work, the authors explicitly discuss how these advances can be applied to different kinds of similar tools in the community.

Original languageEnglish (US)
Article number113495
JournalComputational Materials Science
Volume247
DOIs
StatePublished - Jan 31 2025

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Chemistry
  • General Materials Science
  • Mechanics of Materials
  • General Physics and Astronomy
  • Computational Mathematics

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